The next step of our approach is to combine the heterogeneous radiomic features within a classification scheme that will learn to discriminate between the different patient subgroups and normal individuals. In this paper, we choose to use Support Vector Machines (SVMs) [1]} due to well-known performance when classifying image data, in particular in the case of small sample size. An SVM model corresponds to a transformation of the examples to a hyperspace where a good separation is achieved by the hyperplanes that have the largest distance to the nearest training-data point of any class (so-called functional margin). This ensures that the examples belonging to the different classes are separated as clearly as possible. New cases are then mapped onto that same hyperspace and classified based on their location with respect to the hyperplanes separating the different classes. As such, it is suitable for cardiovascular disease classification as the challenge is precisely to identify subtle changes and differences between normal cardiac characteristics and those of pathological cases.
